1. Field of the Invention
The present invention relates to a data processing system according to the concept of a neural network.
2. Background Information
A neural network is structured with parallel layers of neurons, a neuron 1, being shown in FIG. 3. Neurons in each layer are connected to all neurons in other adjacent layers, so as to input and output data. According to FIG. 3, data 0 is outputted in accordance with the comparison result between the sum of multiplied input data from outside I1, I2, I3 . . . In by weights W1, W2, W3 . . . Wn and threshold .THETA..
Various comparison methods are possible. For example, when normalization function 1[f] is applied, output data 0 is expressed as follows: EQU 0=1[.SIGMA.Wn.multidot.In-.THETA.] (1)
That is, when .SIGMA. Wn.multidot.In exceeds threshold .THETA., the neuron activates so that output data 0 becomes "1"; and when .SIGMA.Wn.multidot.In is smaller than threshold .THETA., output data becomes "0".
A conventional neural network is structured to form neural layers by arranging neurons in parallel and connecting the neural layers in series. Neural layers, for example, comprise three layers, namely, an input layer, a middle layer and an output layer. This is described as Perceptrons, as proposed by Rosenblatt, in which neurons of each layer are synapse-connected to all neurons of other adjacent layers.